Conference Proceeding Article
Embedding deals with reducing the high-dimensional representation of data into a low-dimensional representation. Previous work mostly focuses on preserving similarities among objects. Here, not only do we explicitly recognize multiple types of objects, but we also focus on the ordinal relationships across types. Collaborative Ordinal Embedding or COE is based on generative modelling of ordinal triples. Experiments show that COE outperforms the baselines on objective metrics, revealing its capacity for information preservation for ordinal data.
Euclidean, High-dimensional, Information preservation, Low-dimensional representation, Objective metrics, Ordinal data, data visualization, data mining
Databases and Information Systems | Numerical Analysis and Scientific Computing
Data Science and Engineering
Proceedings of the 2016 SIAM International Conference on Data Mining, Miami, May 5-7
City or Country
LE and LAUW, Hady W..
Euclidean co-embedding of ordinal data for multi-type visualization. (2016). Proceedings of the 2016 SIAM International Conference on Data Mining, Miami, May 5-7. 396-404. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/3358
Copyright Owner and License
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.